EP4425507A1 - Medical data identification system, computer-implemented method thereof, computer program product and computer-readable storage medium - Google Patents

Abstract

The invention relates to a system 20 for identifying medical data mD on the basis of at least one target definition 22 and comprises a data storage medium 25 with a plurality of medical data mD relating to human bodies, a data evaluation module 30 with a computing unit 32, an input device 40 for entering at least one target definition 22, wherein an input of the at least one target definition 22 instructs the data evaluation module 30 to carry out at least the following steps: analyzing the at least one target definition 22 in the computing unit 32, selecting medical data mD from the data storage medium 25 using a selection algorithm AA, checking a predetermined completeness of the medical data mD with a desired medical relevance in the data evaluation module 30 based on the analyzed target definition 23, and providing the selected and/or checked data D at an interface 45 for a user. Furthermore, the invention relates to a computer-implemented method and a computer-readable medium.

Description

The present invention relates to a system for identifying medical data according to patent claim 1, a computer-implemented method according to patent claim 13, a computer program product according to patent claim 14 and a computer-readable storage medium according to patent claim 15.

Technological background

Nowadays, there is a tendency to perform more medical tests on patients than necessary, simply because it is difficult to predict in advance whether something unusual will be found that will affect the diagnosis. Inappropriate tests are also often performed, partly because the doctor in charge is overworked or lacks experience. In the past, each patient's medical record was kept as a physical paper folder locally in a hospital. The notes in the medical record might include, for example, a report of all vital signs, or test results and/or other clinical data collected during the visit, or one or more diagnoses made by the doctor. Often the doctor dictated the note orally into an audio recording device. Later, the notes in the medical record were stored electronically on a storage medium.

If there is a suspicion of an unnatural cause of death, autopsies or post-mortem examinations are often carried out on deceased people in order to examine the corpse and determine the cause of death. This involves generating medical data from the human body with the help of diagnostic examination methods, for example by performing biopsies on the corpse or taking images of the corpse. The medical data generated from the human body in this way has so far only been used locally in the hospital to determine the cause of death of a person. There are a variety of possible uses for the medical data if it could be prepared accordingly and linked to the patient file. This could be used to supply stakeholders such as medical technology companies, court-appointed experts, the pharmaceutical industry and others who require this type of medical data. The quality of the data collected is of great relevance with regard to the satisfaction and requirements of the various stakeholders.

The state of the art is the US2014337057 A1 This discloses a cause of death estimation device comprising a diagnostic information acquisition section, a disease/wound history acquisition section, and a cause of death estimation section. The diagnostic information acquisition section is configured to acquire diagnostic information about image data of a body. The disease/wound history acquisition section is configured to acquire a diagnosis/treatment history of the body before the death of the body. The cause of death estimation section is configured to estimate a direct cause of death of the body and an original cause of death based on the acquired diagnostic information and the acquired diagnosis/treatment history.

The disadvantage of this known solution is that the provision of data is only tailored to a human body.

The state of the art is the KR 101875306 B1 This discloses a disease information providing system that uses medical term clusters. The disease information providing system establishes clinical and causal relationships between medical terms stored in a medical data server and an information providing server, and establishes document clusters related to diseases through the clinical and causal relationships to enable search for diseases by queries. The disease information providing system establishes the clinical and causal relationships related to information such as symptoms, examinations, and treatments of diseases to easily recognize medical information in accordance with diseases. In addition, the disease information providing system groups the medical data by forming disease clusters and similar clusters to provide various and accurate medical information according to the query information by grouping similar diseases.

The state of the art also includes the EP2673722A1 This discloses a computer-implemented method for generating discrete structured data elements for an electronic data set, comprising: receiving an original text that is a representation of a description of a patient encounter provided by a clinician; reformatting the original text to generate a formatted text, extracting one or more clinical facts from the formatted text, wherein the step of extracting comprises using a clinical language understanding engine to parse the formatted text to identify clinical terms in a lexicon of a clinical language, identifying concepts associated with the clinical terms in a formal ontology, and, based on the language knowledge in the formal ontology, finding facts to extract; maintaining a link between each fact and the corresponding portion of the original text; displaying the one or more clinical facts in a fact field on a graphical user interface; in response to a user selecting one of the facts displayed in the fact field, displaying a reference to the corresponding portion of the original text from which the selected fact was extracted to a user on a graphical user interface, whereby the user can review extracted facts with reference to the original text and make one or more changes to one or more of the extracted facts to create a set of clinical facts for the patient contact; storing the set of clinical facts corresponding to the patient contact with extracted facts and any modified facts created by the user as discrete structured data elements in an electronic medical record.

The disadvantage of these known solutions is that the data on diseases is only made available to one patient contact and only locally in a hospital.

Description of the invention

One object of the invention is to avoid at least some of the disadvantages of the prior art. In particular, an improved, intelligent identification system is to be created which can preferably reliably provide the necessary data for a medical question from a large, independent data pool. An improved computer-implemented method is to be created which reliably selects and provides the necessary data, as well as a computer program product and a computer-readable medium therefor.

This object is achieved by the features of the independent patent claims. Advantageous further developments are set out in the figures and in the dependent patent claims.

1. a) Analysieren der mindestens einen Zieldefinition in der Recheneinheit,
2. b) Auswählen von medizinischen Daten aus dem Datenspeichermedium mit einem Auswahlalgorithmus, wobei die ausgewählten Daten verknüpft mit der mindestens einen analysierten Zieldefinition ausgewählt werden,
3. c) Überprüfen einer vorgegebenen Vollständigkeit der medizinischen Daten mit einer gewünschten medizinischen Relevanz im Datenauswertemodul basierend auf der analysierten Zieldefinition,
4. d) Bereitstellen der ausgewählten und/oder überprüften Daten an einer Schnittstelle zur Bereitstellung für einen Benutzer und/oder einem KI -Modul.

A system according to the invention for identifying medical data on the basis of at least one target definition comprises a data storage medium with a plurality of medical data on human bodies, a data evaluation module with a computing unit, which is connected to the data storage medium for data exchange, and an input device for entering at least one target definition, which is connected to the data evaluation module for data exchange, wherein an input of the at least one target definition instructs the data evaluation module to carry out at least the following steps:

1. a) analyzing the at least one target definition in the computing unit,
2. b) selecting medical data from the data storage medium using a selection algorithm, whereby the selected data are linked to the at least one analyzed target definition,
3. c) Checking a given completeness of the medical data with a desired medical relevance in the data evaluation module based on the analyzed target definition,
4. d) Providing the selected and/or verified data at an interface for provision to a user and/or an AI module.

This creates an improved, intelligent identification system that reliably provides only the necessary medical data from at least one entered target definition and only provides medical data with a desired relevance. The medical data preferably comes from many different patients from different hospitals, countries or continents. The at least one target definition specifies the medical query that is to be answered based on data. The at least one target definition therefore has a significant influence on the selection of the contents of the individual medical data to be taken into account. The at least one target definition can either be entered by a user in the input device or transferred to the input device using a physical storage medium or a data network. The system disclosed here identifies those medical data from the data storage medium that are required with regard to the entered target definition in order, for example, to answer a medical query clearly and unambiguously. This optimizes the effort of data analysis and only relevant data is provided. The interface can contain the input device. In addition to human bodies, the method disclosed here can also be applied to other living beings, such as animal bodies.

Providing the selected and/or verified data enables the user and/or the AI (artificial intelligence) module to decide whether the data corresponds to the entered target definition, with the following conclusions or findings following: Either the medical data suitable for fulfilling the at least one target definition with the desired relevance have been output, or information and/or instructions on the completion process carried out are provided and/or information on future data collection with an examination facility is provided. This can be initiated by a user, for example by adjusting the at least one target definition, and/or the AI module independently adjusts the at least one target definition and/or provides the information and/or instructions on the completion process. For example, the analyzed target definition specifies that the provision of the medical data in step d) can only take place if the number of medical data with the desired medical relevance has reached a predetermined relevance threshold, preferably 98%. If the specified threshold is not reached, instructions for the completion process are sent to the interface provided and/or information or instructions on future data collection are provided. This creates a self-improving, intelligent identification system for high-quality medical data.

The selected and/or verified medical data can in turn serve as training data for the AI module, so that the system is intelligent and self-learning. This creates a system for identifying medical data that has high data quality and favors the selection of high-quality data so that at least one target definition is processed quickly and in an optimized manner. In particular, the AI module assigns a medical relevance to the medical data based on the at least one target definition and thus links the medical data to different target definitions. The AI module can in turn be designed to assign a medical relevance to the medical data so that it can be selected more easily in a subsequent target definition.

The AI module can use a deep learning method (using artificial neural networks), in which several layers of artificial neurons link the input variables (feature vector) with the output variable (classification, regression...). Numerous other machine learning methods, random forest algorithms (randomized decision trees) or support vector machines (estimation using support vectors in the vector space of the feature vectors) can also be used, especially to limit the computational effort. The AI module is basically trained with historical medical data and/or with data from an expert database.

The medical data in the data storage medium can, for example, be labeled data or data sets, and include an identification number with which they can be easily identified by the selection algorithm. This makes it easier to identify, for example, whether the medical data comes from a man, with an associated year of birth, blood group, height, place of birth, and other data. The labeled data is typically pseudonymized. The identification number can be used to select an entire data set or data structure of a human body, so that the medical data with the desired relevance can be found quickly and easily.

The selection algorithm can be applied to a database, such as an SQL database, to easily obtain the medical data. The information from the analyzed target definition can be applied sequentially in the selection algorithm to speed up the query. The medical data is advantageously already sorted and stored in the data storage medium, so that sequential querying or retrieval is possible. The selection of medical data is further accelerated. This simplifies the selection of medical data and reduces costs.

The selection algorithm may operate as a grouping algorithm and partition n medical data or identification numbers into k groupings in which each data object or identification number belongs to the group with the closest labeled data or data set. However, values of n-1 < k < n+1 may vary depending on the nature of the at least one analyzed target definition and the underlying medical data. Selecting medical data from the data storage medium is performed using a selection algorithm, wherein the selected data is selected in association with the at least one analyzed target definition.

In computer science, quickselect is a selection algorithm to find the kth smallest element in an unordered group. The identification number can be used as the kth element. Quickselect is related to the sorting algorithm quicksort. Like quicksort, it is efficient in practice and has good average performance. Quickselect and its variants are the selection algorithms that are well suited for efficient real-world implementations.

Quickselect uses the same overall approach as Quicksort, selecting an element as a pivot and splitting the data into two parts based on the pivot, smaller or larger than the pivot accordingly. However, instead of recursing into both sides, as Quicksort does, Quickselect only recurses into one side - the side with the element being searched for. This reduces the average complexity of selecting.

Another example of a selection algorithm is an echo algorithm. To use the echo algorithm as a selection algorithm, each node in the algorithm must have its own identification. Every node starts an echo algorithm at some point, with both the echoes and the explorers carrying the identification of their initiator. Nodes ignore all messages whose initiator has a lower identification than themselves. If an initiator receives an echo with its own identification from all of its neighbors, it knows it has won. All other nodes know they have lost if they have received an explorer with a higher identification than themselves.

In particular, the analysis of the at least one target definition in the computing unit in step a) is carried out on the basis of at least one previously stored, historical target definition from an expert database. The expert database can thus provide previously stored target definitions in order to select the medical data with a high level of efficiency and quality. For this purpose, the data evaluation module is connected to the expert database for the exchange of data.

In particular, the selection algorithm is dynamically adaptable so that the selection of medical data depends on the information of the analyzed target definition. Depending on the knowledge of at least one target definition and the historical target definitions already stored, the selection algorithm can be adapted in order to optimize the computing power and thus the costs in the system.

The expert database can be a self-learning expert database in which relationships or links are stored between the statement requirements formulated in the target definitions or in segments of the target definitions or historical target definitions and the medical data from human bodies evaluated for this purpose. The expert database can generate new relationships or links and create pointers to data sets with medical data in order to obtain the desired medical data in the data storage medium more quickly.

In particular, during the selection process in step b), the selected data is compared with other medical data from the expert database. This allows the efficiency of the selection of medical data to be further improved.

In particular, in step d), the data evaluation module causes the interface to output the selected and/or checked data to a user interface. The expected result for at least one target definition is thus directly accessible to a user and the user can take further actions or make corrections to the target definition in order to obtain the desired complete medical data with the desired relevance. In particular, the user interface comprises an output unit with a display, preferably a touchscreen, so that the selected and/or checked data is available to the user in a user-friendly manner.

Preferably, the input device is provided with framework conditions as data or data sets during input, and in particular made available to the selection algorithm. Framework conditions in this document are understood to mean restrictions on the general definition of the target, e.g. to certain groups of people (age, gender, number, etc.). The framework conditions can define restrictive specifications beyond the general public, e.g. certain target groups (gender, age, weight, sample quantity or number of deceased human bodies). This limits the amount of selection using the selection algorithm and simplifies it, and the medical data can be provided more quickly and at a lower cost.

Preferably, the data storage medium comprises body-related data which are associated with the medical data of at least one human body. This body-related data is data associated with a specific human body (with/without personal reference) and includes, for example, gender, age, height, weight, skin type, eye color, BMI, DNA information, country of origin, etc.

In particular, the medical data originates from a deceased human body, which is preferably created from an autopsy process. Medical data or data sets from deceased bodies are understood to mean, on the one hand, autopsy data and, on the other hand, anonymized or pseudonymized historical body data or data from a patient file of a deceased person, or historical data sets that reflect the medical history of a deceased person and all associated medical data. In addition, the system disclosed here makes it possible to use the medical data created from the human body, which was previously used purely to determine the cause of death, for other purposes.

Preferably, the at least one target definition comprises at least one question. The question can consist of text modules and/or include abbreviations and/or key figures. The question can include medical terms and technical abbreviations and is analyzed using the computing unit. The computing unit can comprise a text recognition program that recognizes formatted and unformatted text modules. The computing unit can be connected to a register with formatted text modules in order to be able to assign the aforementioned information from the question to medical data, for example, and to analyze it. The analyzed target definition is then made available to the selection algorithm in order to select the medical data from the data storage medium. For example, the at least one target definition can comprise several questions that are analyzed as described above and, in particular, are also analyzed in dependence on one another. In particular, the at least one target definition comprises at least one question that can be answered by analyzing medical data or links to the medical data from deceased human bodies.

Alternatively or additionally, the at least one target definition includes medical data. The medical data can be entered by the user in a template for a target definition in the input device, or read in from a storage medium or via a data network. For example, recorded liver values can indicate long-term medication intake, whereby the collection of further medical data in the at least one target definition is recommended because a pharmaceutical company needs this medical data for a study. The corresponding recorded liver values can be entered as medical data into the at least one target definition. Alternatively or additionally, the at least one target definition includes body-related data that could be used additionally for studies and are added, for example, by the user of the at least one target definition.

Alternatively or additionally, the at least one target definition includes at least one medical data acquisition method. It would be conceivable that an artificial hip joint is detected in a CT scan of a corpse, whereby the collection of extended medical data using a further medical data acquisition method is recommended in the at least one target definition, since a medical technology company needs this medical data for internal purposes or the further development of its hip joint prostheses. This makes it possible to provide data that can, for example, simplify an approval process for a new type of hip joint prosthesis, so that medical technology companies can get their products through approval processes more easily and cost-effectively.

As a further example, after the automated evaluation of images taken of the skin of a human body, the detection of hematomas may indicate external influences, whereby further forensic data collection on the corpse is recommended in order to be able to rule out an unnatural cause of death.

The following is a non-exhaustive overview of possible medical data that can be collected from the deceased: whole-body CT image data in high resolution (due to risk considerations, in particular high radiation exposure, this can hardly be carried out on living people), tissue and fluid samples, MR image data, surface scans, angiography, as well as all data about the deceased, e.g. from a patient file filled over the years of life or an electronic health record.

Preferably, the selected medical data is stored in a data structure in the data storage medium. This makes the selected medical data easy to find and can be linked more easily to the at least one target definition. For example, the medical data is grouped into clusters so that it can be easily linked to the expert database. Alternatively or additionally, the body-related data is stored in a data structure in the data storage medium. This also makes this data easy to find and can be linked more easily to the expert database.

In particular, the data storage medium comprises a hierarchical category scheme by means of which the medical data from human bodies can be stored in a structured manner, with each category or category level being assigned a unique category name (e.g. leukocytes), or category level designation (e.g. granulocytes) and/or a value or value interval is assigned, so that a well-detailed data structure is created which can be easily searched with the selection algorithm in order to find the medical data with the desired relevance.

Preferably, the computing unit is designed to generate a data query pattern in step a) using the analyzed target definition. This makes it possible in particular to provide a collection of medical data from a deceased human body that is dependent on a desired relevance, whereby the data query pattern is generated on the basis of specific questions and medical data from deceased human bodies is selected from the data pool of the data storage medium and compared with the respective data query pattern. If it is determined that the data pool in the data storage medium can only provide insufficient medical data, the additional medical data required to answer the question is collected from the deceased human body(s) in a further data collection with an examination device.

The data query patterns are preferably defined by questions, medical and/or body-related data and collection or examination procedures, i.e. for example: (a) a list of questions that are specified by different stakeholders or that arise as a result of a data analysis, and/or b) a list of medical data to be collected for each question, which is continuously updated in line with the state of the art required from a medical point of view, and/or (c) a list of procedures for collecting the medical data, which is continuously optimized with regard to the customer benefit for the stakeholders.

It is advantageous to have a list of questions with associated medical and/or body-related data to be collected, whereby on the one hand the list of questions is continuously expanded and on the other hand the data to be collected for each question is continuously updated (improved). For each data set to be collected, there can be one or more methods to obtain this data. This method(s) is also continuously improved (optimized).

In case the match between the data query pattern and the medical data from human bodies is not complete or does not exist, the current medical data from human bodies can be enriched with historical data from human bodies. The specified completeness of the medical data can be improved in particular by first enriching the incomplete data with data from the patient file and, if necessary, in a next step medical data from other human bodies that, based on statistical criteria, have a sufficiently high similarity to the case in question are further enriched. This process can also be continuously optimized and further improved with the help of the AI module.

1. (a) der Qualität der erhobenen medizinischen Daten im Hinblick auf eine Zufriedenheit des Stakeholders mit der Aussagekraft der medizinischen Daten, d.h. der Stakeholder konnte seine Frage ausreichend beantworten. Andernfalls müsste die mindestens eine Zieldefinition der zu erhebenden medizinischen Daten zur Frage des Stakeholders adaptiert werden. Der Relevanzfaktor ist spezifisch für die Fragestellung des Stakeholders. Die Qualität kann mittels verschiedener Faktoren beschrieben werden. Beispielsweise wurden nicht die richtigen medizinischen Daten erhoben oder die identifizierten medizinischen Daten waren zu ungenau, etc., und/oder
2. (b) dem Aufwand zur Datenidentifikation, um Zeitaufwand und Kosten zu optimieren.

Medically relevant data can therefore also be data that has a sufficient degree of agreement with the data query pattern. The relevance factor or its threshold value is determined from:

1. (a) the quality of the medical data collected in terms of the stakeholder's satisfaction with the informative value of the medical data, i.e. the stakeholder was able to answer his question sufficiently. Otherwise, at least one target definition of the medical data to be collected would have to be adapted to the stakeholder's question. The relevance factor is specific to the stakeholder's question. The quality can be described using various factors. For example, the right medical data was not collected or the identified medical data was too imprecise, etc., and/or
2. (b) the effort required for data identification in order to optimise time and costs.

Preferably, the computing unit is designed to break down the medical data after step a) into individual categories of the category scheme according to a category scheme and thus to generate a data query pattern. The category scheme can serve as a central element of the content-related data analysis. The category scheme is used to determine the medical and/or body-related data, the consideration of which forms the basis for the analysis of the at least one target definition, whereby the category scheme can be structured hierarchically. For example, category level 1: blood count; category blood count - level 2: leukocytes, erythrocytes, hemoglobin; and category blood count - leukocytes - level 3: granulocytes, monocytes, lymphocytes.

The identification system preferably makes it possible to generate a data query pattern from a query or at least one target definition, which makes it possible to identify similar data query patterns that satisfy the at least one target definition using a self-learning expert database. The expert database contains links between the statement requirement formulated in the at least one target definition and the categories of the data records stored in the identification system that are to be evaluated for this purpose.

In particular, the computing unit is designed to divide at least the selected medical data into at least two categories. This allows the selected medical data to be classified differently. Alternatively or additionally, the computing unit is designed to divide the at least one target definition into at least two categories. The computing unit can divide the at least one target definition and its Analyze the framework conditions and break them down into individual categories according to the category scheme, thus creating a data query pattern.

Preferably, the data query pattern comprises an identification part which identifies the relevant data based on the categories so that a reproducible classification can be carried out.

In particular, the data query pattern includes an evaluation part that evaluates the medical data of the identified categories. The values of the data in the identified categories are evaluated using the computing unit. Alternatively or additionally, the evaluation part evaluates data intervals of the identified categories.

In particular, the computing unit proceeds in several stages, whereby in a first step the categories defined by the at least one target definition and/or medical data are identified and in a second step the value intervals relevant to answering the at least one target definition are determined. The computing unit can be designed to identify the categories in a self-learning manner.

Preferably, the selection algorithm is designed to compare the generated data query pattern with the medical data stored in the data storage medium in order to check the specified completeness of the data in step c). The selection algorithm can compare the data query patterns with the categorized data or data sets from human bodies stored in the data storage medium and (a) output the medical data or data sets of the human bodies with a sufficient, e.g. 100%, match, or (b) output the medical data or data sets of the human bodies with an insufficient match with regard to the categorization, or (c) in the event of insufficient completeness, trigger a future identification or data collection according to a data query pattern, wherein the future data collection relates, for example, either to a next autopsy step of an ongoing autopsy and/or to the data collection of future autopsies. The definition of the future data collection can either contain several alternative medical data or, depending on the selection using the selection algorithm from the computing unit, result in a reduction in the medical data collected.

Preferably, the check of a predetermined completeness of the medical data with a desired medical relevance in step c) is associated with the generated data query pattern. This means that it can already be determined from the generated data query pattern whether the predetermined completeness of the medical data can be achieved at all.

Preferably, the data evaluation module is designed to evaluate the data set fields required to answer the analyzed target definition on the basis of the at least one target definition in combination with information from the expert database. A quick analysis of the at least one target definition is possible using the expert database, so that a reproducible selection of the medical data is possible overall.

Preferably, the data evaluation module is designed to supplement a data structure with medical data. This improves the system itself and future target definitions can be analyzed more quickly and with increased quality.

Alternatively or additionally, the data evaluation module is designed to supplement a data structure with body-related data so that the quality is further improved.

In particular, the data evaluation module is designed to store these completed medical data in a further data structure in the data storage medium, and in particular to store them anonymously. This means that the data pool in the data storage medium is accumulated with an inherently improved data quality.

The data evaluation module is advantageously designed to compare a group of data records of deceased bodies with an insufficient match with a group of data records of deceased bodies with a sufficient match, taking into account the data query patterns and, if inherent data query patterns are recognized, the data records of the deceased bodies with an insufficient match using a mathematical model that allows the values or value intervals of the incomplete categories/data to be at least partially completed. The data evaluation module can access the above-mentioned AI module or include its own artificial intelligence, which is arranged, for example, in the computing unit. This makes the data evaluation module a self-learning unit that can automatically complete the data. In particular, the data evaluation module accesses the expert database and exchanges the medical and/or body-related data.

In particular, there is a customer satisfaction information module which relates to customer satisfaction regarding the selection quality of the medical data and makes customer satisfaction-based changes either in the expert database or in the data evaluation module.

Preferably, at least one medical examination device is present which is connected to the interface for the exchange of data and/or control commands. Alternatively or additionally, the at least one medical examination device is connected to the input device for exchanging data and/or control commands. This provides feedback which, in the event of an insufficient database, informs the autopsy device, depending on the target definition, which medical data additionally or no longer needs to be collected within a standard autopsy.

Alternatively or additionally, at least one medical examination device is available, which is connected to an AI module for the exchange of data and/or control commands. The AI module can thus control the medical examination device and thus reproducibly generate medical data with a desired medical relevance.

Preferably, the data evaluation module is designed to generate control commands for at least one medical examination device. This allows the intelligent identification system to directly access and control a medical examination device so that medical data can be collected that has a desired medical relevance.

1. a) Bereitstellen von einzelnen oder mehreren medizinischen Daten aus einem Datenspeichermedium,
2. b) Erhalten von mindestens einer Zieldefinition in einem Datenauswertemodul mit einer Recheneinheit,
3. c) Analysieren der mindestens einen Zieldefinition in der Recheneinheit,
4. d) Auswählen von medizinischen Daten aus dem Datenspeichermedium mit einem Auswahlalgorithmus, wobei die ausgewählten Daten verknüpft mit der mindestens einen Zieldefinition ausgewählt werden,
5. e) Überprüfen einer vorgegebenen Vollständigkeit der medizinischen Daten mit einer gewünschten medizinischen Relevanz im Datenauswertemodul basierend auf der analysierten Zieldefinition,
6. f) Bereitstellen der ausgewählten und/oder überprüften Daten an einer Schnittstelle zur Bereitstellung für einen Benutzer und/oder einem KI-Modul, wobei insbesondere das Datenauswertemodul die Schnittstelle veranlasst, die ausgewählten und/oder überprüften Daten an einer Benutzerschnittstelle auszugeben.

A computer-implemented method according to the invention for identifying medical data based on target definitions comprises at least the following steps:

1. a) Providing individual or multiple medical data from a data storage medium,
2. b) Obtaining at least one target definition in a data evaluation module with a computing unit,
3. c) analyzing the at least one target definition in the computing unit,
4. d) selecting medical data from the data storage medium using a selection algorithm, whereby the selected data are linked to the at least one target definition,
5. e) Checking a given completeness of the medical data with a desired medical relevance in the data evaluation module based on the analyzed target definition,
6. f) Providing the selected and/or verified data at an interface for provision to a user and/or an AI module, wherein in particular the data evaluation module causes the interface to output the selected and/or verified data at a user interface.

This creates an improved, intelligent computer-implemented identification method which reliably provides the necessary medical data from an entered target definition and provides medical data with a desired relevance. Preferred embodiments in the computer-implemented method are already disclosed in the previously described system according to the invention and can be carried out as method steps.

A computer program product according to the invention comprising program instructions which are designed to carry out at least one method described here. The computer program product can be executed on a computing unit and thus process the program instructions step by step in order to provide the selected and/or checked data at an interface. These can then be provided to a user, an AI module, in particular to control an examination device.

A computer-readable storage medium according to the invention comprises the at least one computer program product which, when executed by at least one computing unit, causes the latter to carry out at least one method described herein.

The system and method described here enables the collection of medical data from deceased or living human bodies in a requirement-related or customer-specific manner. When collecting medical data from human bodies, this ensures that relevant data sets are collected as specifically and accurately as possible, since the system also takes body-related data (age, weight, gender, etc.) into account.

In addition, such a system and procedure makes it possible to control and monitor data collection in a targeted manner. This makes it possible to collect only relevant data and to continuously adapt and optimize the data collection process.

This also allows costs to be saved when collecting data, as only relevant data is collected rather than all of it. This allows fewer CT scans, images or biopsy samples to be taken and prepared when collecting data.

The system and the method make it possible to reduce the amount of time required for data collection, since instead of the usual scope, only necessary or relevant data relating to a customer-specific or application-specific case needs to be collected when collecting data from human bodies.

Further advantages, features and details of the invention will become apparent from the following description, in which embodiments of the invention are described with reference to the drawings.

The list of reference symbols is part of the disclosure, as is the technical content of the patent claims and figures. The figures are described in a coherent and comprehensive manner. The same reference symbols mean the same components, while reference symbols with different indices indicate components with the same or similar functions.

The invention is explained in more detail using exemplary embodiments in the following figures. The list of reference symbols is part of the disclosure.

Position specifications such as "top", "bottom", "right" or "left" refer to the corresponding representations and are not to be understood as limiting.

Although the invention is illustrated and described in detail by means of the figures and the associated description, this illustration and this detailed description are to be understood as illustrative and exemplary and not as limiting the invention. It is understood that those skilled in the art can make changes and modifications without departing from the scope of the following claims. In particular, the invention also includes embodiments with any combination of features mentioned or shown above for various aspects and/or embodiments.

The invention also includes individual features in the figures, even if they are shown there in connection with other features and/or are not mentioned above. Furthermore, the expression "comprise" and derivatives thereof do not exclude other elements or steps. Likewise, the indefinite article "a" or "an" and derivatives thereof do not exclude a plurality. The functions of several features listed in the claims can be fulfilled by a unit. The terms "essentially", "about", "approximately" and the like in connection with a property or a value in particular also define precisely the property or precisely the value. All reference signs in the claims are not to be understood as limiting the scope of the claims.

Character description

• Fig. 1 : eine erste Ausführungsform des Systems zur Identifikation von medizinischen Daten auf Basis von einer Zieldefinition in einer schematischen Darstellung,
• Fig. 2: eine zweite Ausführungsform des Systems zur Identifikation von medizinischen Daten auf Basis von einer Zieldefinition in einer schematischen Darstellung,
• Fig. 3: eine dritte Ausführungsform des Systems zur Identifikation von medizinischen Daten auf Basis von einer Zieldefinition mit einer detaillierten schematischen Darstellung eines Datenauswertemoduls, und
• Fig. 4 ein Flussdiagramm zu einem computerimplementierten Verfahren, ausführbar in einem System einer der Ausführungsformen gemäss den Figuren 1 bis 3.

The figures are described in a coherent and comprehensive manner. The same reference symbols mean the same components.

• Fig.1 : a first embodiment of the system for identifying medical data based on a target definition in a schematic representation,
• Fig.2 : a second embodiment of the system for identifying medical data based on a target definition in a schematic representation,
• Fig.3 : a third embodiment of the system for identifying medical data based on a target definition with a detailed schematic representation of a data evaluation module, and
• Fig.4 a flowchart of a computer-implemented method executable in a system of one of the embodiments according to the Figures 1 to 3 .

Implementation of the invention

1. a) Analysieren der Zieldefinition 22 in der Recheneinheit 32,
2. b) Auswählen von medizinischen Daten mD aus dem Datenspeichermedium 25 mit einem Auswahlalgorithmus AA, wobei die ausgewählten Daten aD verknüpft mit der analysierten Zieldefinition 23 ausgewählt werden,
3. c) Überprüfen einer vorgegebenen Vollständigkeit der medizinischen Daten mD mit einer gewünschten medizinischen Relevanz im Datenauswertemodul 30 basierend auf der analysierten Zieldefinition 23,
4. d) Bereitstellen der ausgewählten und/oder überprüften Daten D an einer Benutzerschnittstelle 45 zur Bereitstellung für einen Benutzer B, wobei das Datenauswertemodul 30 eine Ausgabeeinheit 46 der Benutzerschnittstelle veranlasst, die ausgewählten und/oder überprüften Daten D auszugeben.

Figure 1 shows an embodiment of the system 20 for identifying medical data based on a target definition 22 comprising a data storage medium 25 with a data pool of several medical data mD on human bodies, a data evaluation module 30 with a computing unit 32, which is connected to the data storage medium 25 for data exchange, an input device 40 for entering a target definition 22, which is connected to the data evaluation module 30 for data exchange, wherein an input of the target definition 22 instructs the data evaluation module 30 to carry out at least the following steps:

1. a) Analyzing the target definition 22 in the computing unit 32,
2. b) selecting medical data mD from the data storage medium 25 using a selection algorithm AA, whereby the selected data aD are linked to the analyzed target definition 23,
3. c) Checking a given completeness of the medical data mD with a desired medical relevance in the data evaluation module 30 based on the analyzed target definition 23,
4. d) Providing the selected and/or verified data D to a user interface 45 for provision to a user B, wherein the data evaluation module 30 causes an output unit 46 of the user interface to output the selected and/or verified data D.

The medical data mD come from many different living and deceased patients from different hospitals, countries or continents and are anonymized. The target definition 22 is entered by a user B in the input device 40. At the same time, the user B provides framework conditions as data. Framework conditions RB are understood in this document to mean restrictions of the general target definition 22, e.g. to certain groups of people (age, gender, number, etc.).

The system 20 disclosed here identifies those medical data mD from the data storage medium 25 which are required exclusively for answering or fulfilling the entered target definition 22, for example in order to answer a medical question clearly and unambiguously.

The medical data mD in the data storage medium 25 are labeled data or data sets, and include an identification number ID with which they can be easily recorded by the selection algorithm AA. The selection algorithm AA can also select medical data, for example, based on the labeled data sets. This makes it easier to recognize, for example, whether the medical data mD comes from a man, with an associated year of birth, blood group, height, place of birth, and other data. In addition, the data storage medium 25 includes body-related data kD, which is associated with the medical data mD of at least one human body. The body-related data kD includes data such as gender, age, height, weight, skin type, eye color, BMI, DNA information, country of origin, etc.

The data evaluation module 30 is designed to supplement a data structure with medical data mD and with body-related data kD before step d), and to store these completed medical data in a further data structure in the data storage medium 25.

In the present embodiment, the target definition includes a question made up of text modules and key figures. The text modules include medical terms and technical abbreviations and are analyzed using the computing unit 32. The analyzed target definition 23 is then made available to the selection algorithm AA in order to select the medical data mD. Alternatively, the target definition can also include medical data mD.

In this example, the analysis of the target definition 22 in the computing unit 32 in step a) is carried out on the basis of at least one already stored, historical target definition 24 from an expert database 28, which is connected to the data evaluation module 30 for the exchange of data. The selection algorithm AA in the computing unit 32 recognizes the Target definition 22 as an already stored, historical target definition 24 and selects the medical data mD from the data storage medium 25 accordingly. The completeness of the medical data mD is then checked according to step c).

The expert database 28 is a self-learning expert database in which relationships or links between the statement requirement formulated in the target definitions 22 or in segments of the target definitions or historical target definitions 24 and the medical data mD from human bodies evaluated for this purpose are stored. The expert database 28 can generate new relationships or links and create pointers to data sets with medical data mD in order to access the desired medical data mD in the data storage medium 25 more quickly. The data evaluation module 30 is designed to evaluate the data set fields necessary to answer the analyzed target definition 23 on the basis of the target definitions 22 in combination with information from the expert database 28.

The computing unit 32 is designed to generate a data query pattern in step a) using the analyzed target definition 23, wherein data query patterns are generated on the basis of specific questions, and data is selected from the data pool of the data storage medium 25 and compared with the respective data query pattern. Thus, checking the specified completeness of the medical data mD with the desired medical relevance in step c) is associated with the generated data query pattern.

The data evaluation module 30 is designed to compare a group of data records of deceased bodies with an insufficient match with a group of data records of deceased bodies with an adequate match, taking into account the data query patterns, and if inherent data query patterns are recognized, the data records of the deceased bodies with an insufficient match are evaluated using a mathematical model that allows the values or value intervals of the incomplete categories/data to be at least partially completed. The selected and checked data D are provided according to step d).

Figure 2 shows a further embodiment of a system 120 for identifying medical data mD based on a target definition 122, wherein the system 120 is basically functionally and structurally the same as the system 20 according to the Figure 1 The system 120 additionally comprises an AI module 150 and a medical examination device for obtaining medical data 160. The AI module 150 is connected to the medical examination device 160 for the exchange of data and/or control commands.

The target definition 122 here includes a question and medical data mD. The analyzed target definition 123 is made available to the selection algorithm in order to select missing medical data mD. The question can be answered by analyzing medical data mD from deceased human bodies from the data storage medium 25.

For example, the target definition 122 also includes at least one medical data acquisition method. It would be conceivable that an artificial hip joint is detected in a CT scan of a corpse, with the collection of extended medical data mD being carried out with the examination device for medical data acquisition 160 by taking a sample in the area of the artificial hip joint.

In this embodiment, the selected and verified data D are transmitted to an artificial intelligence (AI) module 150 at the interface 45. A user B is not absolutely necessary for the output of the selected or verified data D.

The provision of the selected and verified data D enables the AI module 150 to decide whether the data D corresponds to the entered target definition 122, with the following conclusions or findings following: Either the medical data mD suitable for fulfilling the target definition 122 with the desired relevance were output, or information and/or instructions on the completion process carried out are provided and/or information on future data collection with the examination device 160 is provided. For example, the analyzed target definition 123 specifies that the provision of the medical data mD in step d) can only take place if the number of medical data mD with the desired medical relevance has reached a predetermined threshold of 98%.

The AI module 150 is in turn designed to assign a medical relevance to the medical data mD based on the target definition 122 and thus links the medical data mD with different target definitions 122. The AI module 150 is further designed to assign a medical relevance to the medical data mD if necessary.

The data evaluation module 30 is designed to compare a group of data records of deceased bodies with an insufficient match with a group of data records of deceased bodies with an adequate match, taking into account the data query patterns, and, if inherent data query patterns are recognized, to evaluate the data records of the deceased bodies with an insufficient match by means of a mathematical model which allows the values or value intervals of the incomplete categories / data to be at least partially completed. Data evaluation module 30 can access the above AI module 150 via the interface 145 or can include its own artificial intelligence, which is arranged, for example, in the computing unit 32.

Figure 3 shows a further embodiment of a system 220 for identifying medical data mD based on a target definition 222, wherein the system 220 is largely functionally and structurally structured in the same way as one of the systems 20, or 120 according to Figure 1 or Figure 2 . An embodiment of the data evaluation module 230 is disclosed here in somewhat more detail and comprises a self-learning classification and pattern generation unit 233, a pattern comparison unit 234, an auto-completion unit 235. Further embodiments of the data evaluation module contain only some of the aforementioned units (not shown).

The objective 222 includes a question and framework conditions with which the medical data mD of deceased bodies from a data storage medium 25 are identified, which are suitable for answering or fulfilling the objective definition. The results of past autopsies of at least one autopsy device 260 and historical medical data of deceased bodies are stored in the data storage medium 25, wherein the medical data of the deceased bodies are described by unique category designations or category K (e.g. leukocytes) or category level designations and associated values or value intervals W.

An example of the question in Objective Definition 222 is: Is there evidence that the continuous intake of a pharmaceutical preparation leads to allergies in a 40 to 60 year old patient group?

The self-learning expert database 28 stores the relationships or links between the statement requirement formulated in the target definitions 222 or the segments of the target definitions and the categories of medical data mD or data sets of the deceased bodies to be evaluated, whereby each category consists of a category name (e.g. leukocytes) and a value or value interval.

The question of the target definition 222, which is read in electronically via the input device 40, is analyzed by the self-learning classification and pattern generation unit 233, so that it is possible, using the contents of the self-learning expert database 28, to identify on the one hand the medical data mD or the data sets that are suitable for answering the question, and on the other hand to select the segments of the data sets that contain the information in the sense of the question The classification and pattern generation unit 233 uses the technologies and methods of pattern generation and/or fuzzy logic and/or neural networks and sends the results to a pattern comparison unit 234.

For example, the data evaluation module 30 recognizes which parameters of the data sets are evaluated in order to identify the data set as relevant in terms of the question and which parameters within a data set are analyzed in order to provide an answer to the formulated question of the target definition 222. For example, data sets are identified which indicate continuous intake of a pharmaceutical preparation and parameters are evaluated which may indicate allergies, e.g. immunoglobulins and evaluation of the immunoglobulin E value (IgE).

• Ergebnis 1: Es werden medizinische Daten mD von verstorbenen Körpern mit einer hinreichenden, z.B. 100%-igen, Übereinstimmung identifiziert und ausgegeben, oder
• Ergebnis 2: Es werden medizinische Daten mD von verstorbenen Körpern mit einer nicht hinreichenden Übereinstimmung, bezüglich der Kategorisierung, identifiziert. In diesem Zusammenhang wurden auch medizinische Daten mD identifiziert, welche geeignet sind, die Fragestellung zu beantworten, jedoch sind die jeweiligen Kategorienwerte nicht in den medizinischen Daten mD erfasst, oder
• Ergebnis 3: Es werden medizinische Daten mD von verstorbenen Körpern mit einer unzureichenden Datenbasis identifiziert, so dass eine zukünftig erweiterte Datenerhebung gemäß eines Datenabfragemusters ausgelöst werden kann bzw. muss.

The pattern comparison unit 234 compares the data query pattern with the medical data mD or data sets stored in the data storage medium 25 and, after evaluating the respective data set segments, comes to three possible results, which are output via an output unit 46 by means of a display:

• Result 1: Medical data mD from deceased bodies with a sufficient, e.g. 100%, match are identified and output, or
• Result 2: Medical data mD of deceased bodies with insufficient agreement regarding categorization are identified. In this context, medical data mD were also identified which are suitable for answering the question, but the respective category values are not recorded in the medical data mD, or
• Result 3: Medical data mD from deceased bodies with an insufficient database are identified so that future expanded data collection can or must be triggered according to a data query pattern.

In the case of a result 2, it is possible to complete incomplete data records using an auto-completion unit 235. To do this, the group of data records of the medical data mD with sufficient agreement is searched for data patterns that can be recognized in the group of data records with insufficient agreement, taking into account the data query patterns, in order to then complete the values or value intervals of the incomplete categories / variables using a mathematical model that allows the values or value intervals of the incomplete categories / variables to be at least partially completed.

In addition, a feedback unit, for example as an AI module 150, is present, which makes it possible to determine the future necessary steps of the autopsy device 260 in the case of result 3.

Likewise, system 220 has a customer satisfaction information unit 236 which, on the one hand, contributes to the effect of the self-learning classification and pattern generation unit 233 in terms of improving the data query pattern generation, and, on the other hand, in terms of improving the relationships of the self-learning expert database 28.

1. a) Bereitstellen von einzelnen oder mehreren medizinischen Daten aus einem Datenspeichermedium,
2. b) Erhalten von mindestens einer Zieldefinition in einem Datenauswertemodul mit einer Recheneinheit,
3. c) Analysieren der mindestens einen Zieldefinition in der Recheneinheit,
4. d) Auswählen von medizinischen Daten aus dem Datenspeichermedium mit einem Auswahlalgorithmus, wobei die ausgewählten Daten verknüpft mit der mindestens einen Zieldefinition ausgewählt werden,
5. e) Überprüfen einer vorgegebenen Vollständigkeit der medizinischen Daten mit einer gewünschten medizinischen Relevanz im Datenauswertemodul basierend auf der analysierten Zieldefinition,
6. f) Bereitstellen der ausgewählten und/oder überprüften Daten an einer Schnittstelle zur Bereitstellung für einen Benutzer und/oder ein KI-Modul, wobei insbesondere das Datenauswertemodul eine Benutzerschnittstelle veranlasst, die ausgewählten und/oder überprüften Daten an der Benutzerschnittstelle auszugeben.

Figure 4 shows a schematic representation of the computer-implemented method for identifying medical data based on target definitions, comprising at least the following steps:

1. a) Providing individual or multiple medical data from a data storage medium,
2. b) Obtaining at least one target definition in a data evaluation module with a computing unit,
3. c) analyzing the at least one target definition in the computing unit,
4. d) selecting medical data from the data storage medium using a selection algorithm, whereby the selected data are linked to the at least one target definition,
5. e) Checking a given completeness of the medical data with a desired medical relevance in the data evaluation module based on the analyzed target definition,
6. f) Providing the selected and/or verified data at an interface for provision to a user and/or an AI module, wherein in particular the data evaluation module causes a user interface to output the selected and/or verified data at the user interface.

The aforementioned computer-implemented method can be carried out in a system 20, 120, 220 disclosed here and in particular with the data evaluation module 30 and with at least one processor of the computing unit 32.

The medical data mD comes, among other things, from a deceased human body and is created using an autopsy process.

The data storage medium 30 comprises a hierarchical category scheme by means of which the medical data mD from human bodies can be stored in a structured manner, wherein each category or category level comprises a unique category designation (e.g. leukocytes) or category level designation (e.g. granulocytes) and a value or a value interval.

In addition, there is a list of questions with associated medical and/or body-related data to be collected (kD), whereby on the one hand the list of questions is continuously expanded in the procedure and on the other hand the data to be collected for each question in the procedure is continuously updated.

In step c), a data query pattern is created using the analyzed target definition 23, whereby data query patterns are generated on the basis of specific questions, and data is selected from the data pool of the data storage medium 25 and compared with the respective data query pattern.

In the event that the match between the data query pattern and the medical data mD from human bodies is not complete or does not exist, after step e) the current data from human bodies are enriched with historical data from human bodies. The specified completeness of the medical data can be improved in particular by first enriching the incomplete data with data from the patient file and, if necessary, in a next step also enriching medical data from other human bodies that have a sufficiently high similarity to the case in question based on statistical criteria. This process can also be continuously optimized and further improved with the help of an AI module.

Furthermore, the computing unit 32 is designed to divide at least the selected medical data mD into categories. The data query pattern comprises an identification part, which identifies the relevant data based on the categories, and an evaluation part, which evaluates the medical data of the identified categories. The values of the data in the identified categories are evaluated using the computing unit. The computing unit 32 proceeds in several stages, with the categories defined by the at least one target definition and/or medical data being identified in a first step and the value intervals relevant to answering the at least one target definition being determined in a second step.

The selection algorithm AA is designed to compare the generated data query pattern with the medical data mD stored in the data storage medium 35 in order to check the specified completeness of the data in step e). Selection algorithm AA compares the data query patterns with the categorized data or data sets from human bodies stored in the data storage medium 25 and (a) outputs the medical data or data sets of the human bodies with a sufficient, e.g. 100%, match, or (b) outputs the medical data or data sets of the human bodies with an insufficient match with regard to the categorization (e.g. values at certain category levels are not available), or (c) in the event of insufficient completeness, triggers a future identification or data collection according to a data query pattern, whereby the future data collection relates, for example, either to a next autopsy step of an ongoing autopsy and/or to the data collection of future autopsies. The definition of the future data collection can either contain several alternative medical data or, depending on the selection with the selection algorithm from the computing unit, result in a reduction in the medical data collected.

A computer program product comprises program instructions which are designed to carry out at least one method described here. The computer program product can be executed in a computing unit 32 which is connected to or integrated into a data evaluation module.

• Erstellung von Datenabfragemustern basierend auf mindestens einer Zieldefinition;
• Abgleich der Datenabfragemuster mit vorhandenen Daten aus menschlichen Körpern.
• Entscheidung, ob die analysierte Zieldefinitionen durch die vorhandenen Daten aus menschlichen Körpern erfüllt werden können oder ob eine weitere Erhebung von Autopsiedaten erforderlich ist.
• Entscheidung, welche und wie viele medizinische Daten aus menschlichen Körpern im Zweifelsfall zusätzlich identifiziert und erfasst werden müssen.

In one possible embodiment, the program instructions cause a processor and its peripherals to map at least the following steps in the aforementioned identification systems 20, 120, 220 during their sequential processing:

• Creating data query patterns based on at least one goal definition;
• Matching data query patterns with existing data from human bodies.
• Decision as to whether the analyzed target definitions can be met by the available data from human bodies or whether further collection of autopsy data is required.
• Decision as to which and how much medical data from human bodies must be additionally identified and recorded in case of doubt.

In addition, the identification system is self-learning, which continuously develops and improves the identification system based on the knowledge gained and the data collected.

A computer-readable storage medium comprises the at least one computer program product which, when executed by at least one computing unit, causes the latter to carry out at least one of the methods described herein.

List of reference symbols

20

Identification system

22

Goal definition

23

analyzed goal definition

24

historical target definition

25

Data storage medium

28

Expert database

30

Data evaluation module

32

Computing unit

40

Input device

45

User interface

46

Output unit

120

Identification system

122

Goal definition

123

analyzed goal definition

145

interface

150

AI Module

160

Investigation facility

220

Identification system

222

Goal definition

230

Data evaluation module

233

Classification and pattern generation unit

234

Pattern matching unit

235

Autocompletion unit

236

Customer satisfaction information unit

260

Autopsy device

mD

medical data

kD

body-related data

aD

selected data

D

verified data

A.A.

Selection algorithm

B

user

ID

Identification number

K

Categories

W

Values/value intervals

RB

Framework conditions

Claims (15)

System (20; 120; 220) for identifying medical data (mD) based on at least one target definition (22; 122; 222) comprising a data storage medium (25) with several medical data (mD) on human bodies, a data evaluation module (30; 230) with a computing unit (32) which is connected to the data storage medium (25) for data exchange, an input device (40) for entering at least one target definition (22; 122; 222), which is connected to the data evaluation module (30; 230) for data exchange, wherein an input of the at least one target definition (22; 122; 222) instructs the data evaluation module (30; 230) to carry out at least the following steps: a) analyzing the at least one target definition (22; 122; 222) in the computing unit (32), in particular on the basis of at least one already stored historical target definition (24) from an expert database (28), b) selecting medical data (mD) from the data storage medium (25) using a selection algorithm (AA), wherein the selected data are selected in conjunction with the at least one analyzed target definition (23) and in particular are compared with further medical data (mD) from the expert database (28), c) checking a predetermined completeness of the medical data (mD) with a desired medical relevance in the data evaluation module (30; 230) based on the analyzed target definition (23), d) providing the selected and/or verified data (D) at an interface (45; 145) for a user and/or an AI module (150), wherein in particular the data evaluation module (30; 230) causes the interface (45; 145) to output the selected and/or verified data at a user interface (45; 145). System according to claim 1, characterized in that the data storage medium (25) comprises body-related data (kD) which are associated with the medical data (mD) of at least one human body, and in particular the medical data (mD) originate from a deceased human body, wherein in particular the medical data (mD) and/or body-related data (kD) are anonymized data. System according to claim 1 or 2, characterized in that the at least one target definition (22; 122; 222) comprises at least one question and/or medical data (mD) and/or body-related data (kD) and/or at least one medical data acquisition method. System according to one of the preceding claims, characterized in that the selected medical data (mD) and/or body-related data (kD) are stored in a data structure in the data storage medium (25). System according to one of the preceding claims, characterized in that the computing unit (32) is designed to generate a data query pattern in step a) using the analyzed target definition (23). System according to claim 5, characterized in that the computing unit (32) is designed to break down the medical data (mD) and/or the at least one target definition (22; 122; 222) into individual categories (K) of the category scheme according to a category scheme and thus generates a data query pattern, and in particular is designed to divide at least the selected medical data (mD) into at least two categories (K). System according to claim 5 or 6, characterized in that the data query pattern comprises an identification part which identifies the relevant data on the basis of the categories (K), and in particular an evaluation part which evaluates the data and/or data intervals (W) of the identified categories. System according to one of claims 5 to 7, characterized in that the selection algorithm is designed to compare the generated data query pattern with the medical data (mD) stored in the data storage medium (25) in order to check the predetermined completeness of the data in step c) and/or the checking of a predetermined completeness of the medical data (mD) with a desired medical relevance in step c) is associated with the generated data query pattern. System according to one of the preceding claims, characterized in that the data evaluation module (30; 230) is designed to evaluate the data record fields necessary to answer the analyzed target definition (23) on the basis of the at least one target definition (22; 122; 222) in combination with information from the expert database (28). System according to one of the preceding claims, characterized in that the data evaluation module (30; 230) is designed to supplement a data structure with medical data (mD) and/or body-related data (kD) and in particular to to store the completed medical data in a further data structure in the data storage medium (25), in particular to store it anonymously. System according to one of the preceding claims, characterized in that at least one medical examination device (160; 260) is present, which is connected to the interface (45; 145) and/or to the AI module for the exchange of data (D) and/or control commands. System according to one of the preceding claims, characterized in that the data evaluation module (30; 230) is designed to generate control commands for at least one medical examination device (160; 260). Computer-implemented method for identifying medical data (mD) based on target definitions (22; 122; 222; 23; 24) comprising at least the following steps: a) providing individual or multiple medical data (mD) from a data storage medium (25), b) obtaining at least one target definition (22; 122; 222) in a data evaluation module (30; 230) with a computing unit (32), c) analyzing the at least one target definition (22; 122; 222) in the computing unit (32), in particular on the basis of at least one already stored historical target definition (24) from an expert database (28) d) selecting medical data (mD) from the data storage medium (25) using a selection algorithm (AA), wherein the selected data are selected in conjunction with the at least one target definition (22; 122; 222) and in particular are compared with further medical data (mD) from the expert database (28), e) checking a predetermined completeness of the medical data (mD) with a desired medical relevance in the data evaluation module (30; 230) based on the analyzed target definition (23), f) providing the selected and/or verified data (D) at an interface (45; 145) for provision to a user and/or an AI module, wherein in particular the data evaluation module (30; 230) causes the interface (45; 145) to output the selected and/or verified data (D) at a user interface (45; 145). Computer program product comprising program instructions which are designed to carry out at least one method according to claim 13. Computer-readable storage medium comprising at least one computer program product which, when executed by at least one computing unit (32), causes the latter to carry out at least one method according to claim 13.

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